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An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness.
Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Bailey C, Sadda S, Taylor P, Rudnicka AR. Tufail A, et al. Among authors: sadda s. Health Technol Assess. 2016 Dec;20(92):1-72. doi: 10.3310/hta20920. Health Technol Assess. 2016. PMID: 27981917 Free PMC article.
Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders.
Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka AR. Tufail A, et al. Among authors: sadda s. Ophthalmology. 2017 Mar;124(3):343-351. doi: 10.1016/j.ophtha.2016.11.014. Epub 2016 Dec 23. Ophthalmology. 2017. PMID: 28024825 Free article.
Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.
Ipp E, Liljenquist D, Bode B, Shah VN, Silverstein S, Regillo CD, Lim JI, Sadda S, Domalpally A, Gray G, Bhaskaranand M, Ramachandra C, Solanki K; EyeArt Study Group. Ipp E, et al. Among authors: sadda s. JAMA Netw Open. 2021 Nov 1;4(11):e2134254. doi: 10.1001/jamanetworkopen.2021.34254. JAMA Netw Open. 2021. PMID: 34779843 Free PMC article. Clinical Trial.
794 results